Tumor location, genomic alterations, and radiomic features as predictors of survival in glioblastoma: a Multi-Modal analysis.
Purpose: This study aims to identify the impact of tumor location on the survival of glioblastoma (GBM) patients and the associated genetic alterations, using MRI scans from The Cancer Imaging Archive (TCIA) and genomic data from The Cancer Genome Atlas (TCGA). It also seeks to uncover non-invasive...
| Publicado en: | Neuroradiology Vol. 67; no. 10; pp. 2713 - 2726 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189357978&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189357978 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Oct2025 vid: 67 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189357978 187276202 189357978 189357978 10.1007/s00234-025-03742-7 189357978 ppf: 2713 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Tumor location, genomic alterations, and radiomic features as predictors of survival in glioblastoma: a Multi-Modal analysis. aug: au: Kundal, Kavita Rao, K Venkateswara Dhanda, Sandeep Kumar Kumar, Neeraj Kumar, Rahul affil: https://ror.org/01j4v3x97 Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, India sug: subj: Glioma Prognosis Glioma Familial and Genetic Glioma Pathology Genomics Mutation Radiomics Overall Survival Tumor Markers, Biological Magnetic Resonance Imaging Human India Funding Source Adult Middle Age Aged Aged, 80 and Over Descriptive Statistics Survival Analysis Kaplan-Meier Estimator Cox Proportional Hazards Model Fisher's Exact Test Wilcoxon Rank Sum Test Data Analysis Software Frontal Lobe Neoplasms Phosphatases Blood Gene Expression Disease Progression Parietal Lobe Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over ab: Purpose: This study aims to identify the impact of tumor location on the survival of glioblastoma (GBM) patients and the associated genetic alterations, using MRI scans from The Cancer Imaging Archive (TCIA) and genomic data from The Cancer Genome Atlas (TCGA). It also seeks to uncover non-invasive radiomic markers related to poor survival outcome for improved prognosis and treatment planning. Methods: We analysed pre-operative MRI scans and genomic data from 123 GBM patients (TCIA and TCGA). Tumor locations were determined using our in-house tool, "tumorVQ", followed by Kaplan-Meier survival analysis based on tumor position. Genomic analysis included somatic mutations, copy number variations, fusion genes, and differential gene expression to identify factors linked to poor survival. We extracted radiomic features from T1ce MRI scans using pyRadiomics to analyse their relationship with survival outcomes. Results: Kaplan-Meier analysis showed worse survival for tumors in the parietal lobe compared to other lobes, especially frontal lobe tumors. Genomic analysis revealed high prevalence of PTEN mutations, and exclusive fusion genes FGFR3-TACC3 and EGFR-SEPT14 in parietal lobe tumors. Differential gene expression showed upregulation of PITX2, HOXB13, and DTHD1, linked to tumor progression, while ALOX15 downregulation increased relapse risk. Copy number alterations, like LINC00290 deletions, were associated with aggressive parietal lobe tumors. Radiomic features, lower GLDM DependanceEntropy (LLL) and higher FirstOrder Mean (HLL), were strongly linked to increase risk. Conclusion: This study highlights poor survival outcomes in GBM patients with parietal lobe tumors. Key genetic alterations, such as PTEN mutations and fusion genes, drive tumor progression and chemoresistance in parietal lobe tumors. The association between radiomic features and survival indicates their potential as non-invasive prognostic biomarkers, which could aid in personalized treatment and improved patient management. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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